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BVSR-EvD:模糊视频时空超分辨率与通过扩散模型的事件

Wenming Weng, Yueyi Zhang, Wenhao Xu

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

|December 17, 2025

概括

本研究介绍了BVSR-EvD,这是一种使用事件摄像头和扩散模型进行模糊视频恢复的新方法. 它显著提高了视频超分辨率,改善了空间和时间细节.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
信号处理信号处理

背景情况:

由于数据有限,从低分辨率,低率和模糊源恢复视频是困难的.
现有的方法在不够的先前数据中扎,无法有效恢复.

研究的目的:

提出BVSR-EvD,一种用于模糊视频时空超分辨率的新方法.
为了利用事件摄像头和扩散模型进行增强的视频恢复.

主要方法:

BVSR-EvD使用事件-视频双模式来提取三个数据先验:运动 (事件),内容 (视频) 和物理 (集成).
三叉扩散模型 (Trident-DM) 将解分解为三叉脱和自适应性自我组成阶段.
超级网络提取先验,并学习重量地图动态整合它们.

主要成果:

BVSR-EvD可以达到8倍的空间超分辨率和64倍的时间超分辨率.
该方法在公共视频数据集上与现有技术相比,显示出更高的性能.
综合的priors增强了时间稳定性,内容保存和细节性.

结论:

BVSR-EvD有效地解决了模糊视频恢复的挑战.
拟议的方法在空间和时间超分辨率上都提供了显著的改进.
这项工作突出了将事件摄像头和扩散模型结合起来,实现先进的视频处理的潜力.

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